A tree wired network topology inference method and related device
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SOUTH CHINA UNIV OF TECH
- Filing Date
- 2026-02-13
- Publication Date
- 2026-06-02
Smart Images
Figure CN121711258B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of wired network topology sensing technology, and in particular to a method and related equipment for inferring the topology of a tree-like wired network. Background Technology
[0002] In applications such as power line communication, distribution network monitoring, and wired network operation and maintenance, network topology directly affects signal transmission characteristics, network management strategies, and fault location effectiveness. However, in actual engineering projects, wired network lines are often concealed, historical data is missing or incomplete, and the network structure may change over time; simultaneously, some end nodes are difficult to connect to measurement equipment, leaving network topology information in a state of long-term unknown or uncertainty. Therefore, achieving effective inference of the tree-like wired network topology without disrupting the original network structure has significant engineering implications.
[0003] In existing technologies, one type of method obtains the direct propagation distance between multiple end nodes of the network and reconstructs the topology accordingly by measuring the distances between them. This type of method can obtain relatively stable results when there are enough measurement ports, but it has high requirements for port integrity, is complex to deploy, and is costly, making it difficult to apply to actual power line or communication cable networks. Another type of method uses reflection observation from a single measurement port to infer the network structure based on the propagation distance information in the reflected signal. This type of method is simple to deploy, but when the network is large or the branch structure is complex, there may be a large number of topologies that match the single-port observation results, which can easily lead to topological ambiguity and significantly increase computational complexity.
[0004] Furthermore, under limited measurement port conditions, even with the introduction of observation information from multiple measurement ports, problems still exist such as the difficulty in stably correlated reflection events across ports, the susceptibility to accidental matching, and the lack of global consistency verification, which affect the reliability and feasibility of topology inference.
[0005] Therefore, there is an urgent need for a tree-structured wired network topology inference scheme that can effectively reduce topology inference uncertainty and maintain achievable computational complexity without requiring all end nodes to participate in the measurement and under the condition of limited measurement ports. Summary of the Invention
[0006] The main objective of this application is to propose a tree-structured wired network topology inference method, electronic device, storage medium, and program product based on a limited number of measurement ports. This method can effectively reduce the uncertainty of topology inference and improve the stability of the inference process and the reliability of the results by using only a portion of the accessible ports in the network for measurement.
[0007] To achieve the above objectives, one aspect of this application proposes a method for topology inference of a tree-structured wired network, the method comprising:
[0008] Acquisition steps: From the M selected measurement ports in the tree-structured wired network, obtain the set of real reflection events corresponding to each measurement port and the direct propagation distance between each pair of the M measurement ports, where M is an integer greater than 1;
[0009] Construction steps: Based on the direct propagation distance, construct a tree-like initial connectivity structure containing all M measurement ports;
[0010] Update steps: Based on the cross-port consistency between the actual reflection events of different measurement port pairs, identify and add at least one intermediate connection point in the initial connectivity structure to form an updated connectivity structure;
[0011] Simulation and differential steps: Perform propagation response simulation on the updated connectivity structure to obtain the set of simulated reflection events corresponding to each measurement port; match the set of real reflection events for each port with the set of simulated reflection events corresponding to the port, and remove reflection events that can be explained by the updated connectivity structure to obtain the set of differential reflection events corresponding to each measurement port.
[0012] Growth and verification steps: Based on the updated connectivity structure, at least one growth point is determined, and a candidate growth distance set is generated at each growth point based on the differential reflection event set; driven by the candidate growth distance set, verification-based structure expansion is performed at the corresponding growth point, and the expanded candidate topology structure is filtered using the global consistency evaluation function, and the current topology structure is iteratively updated.
[0013] Termination step: When the preset termination condition is met, stop the iteration and output the current topology as the inferred topology of the tree-like wired network.
[0014] In some embodiments, the update step specifically includes:
[0015] For at least one pair of measurement ports, a pair of reflection events is selected from their respective sets of real reflection events. Based on a first distance consistency constraint between the round-trip propagation distance of the reflection event pair and the direct propagation distance of the corresponding port pair, candidate connection positions on the path of the port pair are determined.
[0016] When the same candidate connection location is supported by at least two different measurement port pairs, the candidate connection location is determined as an intermediate connection point and the intermediate connection point is added to the initial connectivity structure.
[0017] In some embodiments, during the simulation and difference steps, the condition for determining that a real reflection event can be explained by the updated connectivity structure is:
[0018] In the set of simulated reflection events, there exists a simulated reflection event such that the difference between the round-trip propagation distance between the real reflection event and the simulated reflection event does not exceed a first distance tolerance threshold, and the difference in reflection amplitude does not exceed an amplitude tolerance threshold.
[0019] In some embodiments, during the growth and verification step, the differential reflection event set is used at the growth point. Generate a set of candidate growth distances. Specifically, it includes:
[0020] For each measurement port Calculate the update distance for each event in its differential reflection event set, where the update distance is the round-trip propagation distance of the event minus twice the measurement port distance. To the growth point The distance;
[0021] Within the preset second distance tolerance range, the distance values that occur together from the updated distances of different measurement ports are statistically analyzed;
[0022] The common distance values are used to form the growth points. The corresponding candidate growth distance set .
[0023] In some embodiments, during the growth and verification step, at the growth point The specific implementation of the verification-based structure extension includes:
[0024] The candidate growth distance set Candidate distances are sorted in ascending order of value;
[0025] Try each candidate distance in order of sorting. At the growth point A temporary external connection with a length of Candidate branches are used to form a temporary candidate topology;
[0026] Calculate the global consistency evaluation function value of the temporary candidate topology. ;
[0027] like ,in If the value of the global consistency evaluation function for the current topology is found, then the temporary candidate topology is accepted as the new current topology, and the simulation and differential steps, as well as the operation of generating the candidate growth distance set in this step, are immediately re-executed to update the differential reflection event set and the candidate growth distance set before continuing the iteration; otherwise, the candidate distance is rejected. And try the next candidate distance.
[0028] In some embodiments, the global consistency evaluation function The construction method is as follows:
[0029] For each measurement port The actual set of reflection events and the simulated set of reflection events under the current topology S are respectively transformed into continuous reflection amplitude curves and;
[0030] The integral sum of the squares of the differences between the reflection amplitude curves and the simulated amplitude curves at all measurement ports over the observation distance range is used as the global consistency evaluation function. The value of .
[0031] In some embodiments, the construction step of building a tree-like initial connected structure based on the direct propagation distance is implemented by a root adjacency algorithm or a minimum spanning tree algorithm, and the constructed structure satisfies the following: the path length of any two measurement ports in the structure is consistent with the direct propagation distance between them within the allowable error range.
[0032] To achieve the above objectives, another aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above.
[0033] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.
[0034] To achieve the above objectives, another aspect of the embodiments of this application proposes a computer program product, including a computer program that, when executed by a processor, implements the method described above.
[0035] Compared with the prior art, the present invention has at least the following beneficial effects:
[0036] 1) Reduced port deployment requirements: Without requiring all end nodes in the network to participate in the measurement, the direct propagation information and reflection response information obtained by a limited number of measurement ports can be used to infer the connection relationship and branch length of the tree-like wired network. This is suitable for engineering environments where end ports cannot be accessed or measurement conditions are limited.
[0037] 2) Improve the reliability of structural confirmation: By performing cross-port pair consistency verification on reflection events of different ports, the common confirmation and structural supplementation of intermediate connection points within the path can be achieved, avoiding accidental matching and misjudgment caused by relying on a single port pair or local observation, thereby reducing topological ambiguity and improving inference stability.
[0038] 3) Differential-driven incremental reconstruction: By propagation response simulation and event matching elimination, the reflection events that can be explained by the current connectivity structure are distinguished from the set of differential reflection events that cannot be explained, so that the evidence of unknown branches / unknown nodes is separated from the explained components; on this basis, the topology structure is gradually grown and verified by differential events, thereby improving the interpretability and robustness of the inference process.
[0039] 4) Global consistency constraints and controllable complexity: The candidate structure update is verified and screened through global consistency evaluation, which reduces invalid candidate expansion and structure backtracking, and avoids large-scale exhaustive matching of unknown topologies, thus balancing inference accuracy and achievable computational complexity under limited measurement conditions. Attached Figure Description
[0040] Figure 1 This is a flowchart of a tree-structured wired network topology inference method based on a limited number of measurement ports provided in an embodiment of this application;
[0041] Figure 2 This is a schematic diagram of the overall process of the power line network topology reconstruction method based on multi-port reflection response consistency analysis provided in the embodiments of this application.
[0042] Figure 3 This is a complete topology diagram of the example power line network in the embodiments of this application, used to illustrate the connection relationship between each measurement port, branch node and branch in the example network.
[0043] Figure 4 This is a schematic diagram of the initial connectivity structure constructed by tree metric analysis based on the known direct propagation distance between known measurement port pairs in this embodiment of the application.
[0044] Figure 5 This is an updated connectivity diagram obtained by refining the internal nodes of the path based on the initial connectivity structure and combining the consistency analysis of multi-port reflection events in the embodiments of this application.
[0045] Figure 6 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation
[0046] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit it. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this application; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this application as detailed in the appended claims.
[0047] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.
[0048] In applications such as power line communication, smart power distribution, and building cabling, accurately knowing the physical topology of wired networks (i.e., line connections and lengths) is crucial for network performance optimization, fault diagnosis, resource management, and secure operation and maintenance. However, in actual engineering projects, network lines are often laid in concealed locations, drawings are missing, or do not match the actual situation, and some end nodes are difficult to connect to measurement equipment, resulting in the network topology remaining unknown or incompletely known for a long time.
[0049] Existing network topology inference techniques are mainly divided into two categories: one is based on measuring the direct propagation distance (such as latency) between multiple points. This method requires deploying measurement devices at multiple (ideally all) end nodes of the network, determining the relative distance by measuring the signal propagation time between nodes, and then reconstructing the topology. This type of method can obtain accurate results when all measurement points are available, but it has high deployment costs and poor flexibility, making it difficult to apply in many practical scenarios where end nodes cannot be accessed. The other category is based on single-port reflection measurement, which infers the topology by analyzing the reflected signals received at a single measurement point and generated by points of network impedance discontinuity (such as branch points or ends). This method is simple to deploy, but when the network structure is complex, the reflected signals overlap and are difficult to interpret, often resulting in "ambiguity" problems where multiple topologies match the observed data, leading to poor uniqueness and reliability of the inference results.
[0050] In recent years, some studies have attempted to combine reflection information from a limited number of measurement ports. However, simply combining observation data from multiple ports still faces challenges: reflection events observed from different ports are difficult to correlate correctly; local matching may produce accidental consistency, leading to incorrect structural judgments; and the lack of an effective evaluation mechanism for the global interpretability of candidate topologies makes the inference process unstable, with computational complexity increasing sharply with network size.
[0051] In view of this, this application provides a method, electronic device, storage medium, and program product for topology inference of a tree-like wired network based on a limited number of measurement ports. Under the condition of a limited number of measurement ports, this scheme comprehensively utilizes the direct propagation distance constraint between measurement port pairs and the consistency information of multi-port reflection events. First, it constructs the initial connectivity structure of the tree-like network. Then, it determines the intermediate connection points within the path by verifying consistency across port pairs to obtain an updated connectivity structure. Based on the updated connectivity structure, it forms a differential reflection event set through propagation response simulation and event matching elimination. Driven by the differential reflection event set, it performs verification-based growth at the growth point set, combined with global consistency evaluation. The structural extensions are filtered, and the final output is the topology inference result of the tree-like wired network. This application does not require all end nodes in the network to participate in the measurement, which can reduce the uncertainty of topology inference and maintain achievable computational complexity in engineering environments with limited measurement conditions.
[0052] The tree-structured wired network topology inference method provided in this application relates to the field of wired network topology sensing technology. This method can be applied to terminals, servers, or software running on either a terminal or server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, or in-vehicle terminal, but is not limited to these. The server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network. The software can be an application implementing the tree-structured wired network topology inference method, but is not limited to the above forms.
[0053] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.
[0054] See Figure 1 and Figure 2 This embodiment provides a method for topology inference of a tree-like wired network based on a limited number of measurement ports, including the following steps:
[0055] Step S1: Obtain the set of reflection events. Select a subset of end ports that can be connected to the measurement device as the set of measurement ports. Reflection measurements were performed at each measurement port, and the set of reflection events was extracted. ,in, This represents the round-trip propagation distance (or the equivalent distance calculated from the round-trip propagation delay) corresponding to the reflected event; therefore, its one-way distance can be used... express. This corresponds to the amplitude feature.
[0056] Step S2: Obtain the direct propagation distance matrix. Obtain the measurement port pair. direct transmission distance And form a direct propagation distance matrix. The direct propagation distance can be obtained by converting the direct propagation delay and propagation speed or by other equivalent methods; this embodiment does not limit this.
[0057] Step S3: Initial connectivity structure construction. Based on the direct propagation distance matrix. Construct a tree-like initial connectivity structure containing all measurement ports. , making The path length between any two measurement ports and the corresponding Consistent within the allowable error range. This step is used to determine the connectivity framework at the measurement port level; intermediate connection points within the path may be temporarily incomplete.
[0058] Step S4: Cross-port consistency verification and intermediate link consistency determination yield the updated connectivity structure. In the initial connectivity structure... Based on this, for different measurement ports From the set of reflection events and Select a pair of reflection events, if they satisfy the distance consistency constraint:
[0059]
[0060] Then its corresponding position is used as a port pair. Evidence of candidate connection locations on the path is collected, but not immediately confirmed. Further, cross-port pair consistency verification is performed on the candidate connection locations generated by each port pair: only when the same candidate location appears repeatedly across multiple different port pairs and is mutually compatible, and receives common support that satisfies a threshold, is that location confirmed as an intermediate connection point within the path, and... By supplementing the structure along the corresponding path, an updated connectivity structure including intermediate connection points is obtained. .
[0061] Step S5: Update the propagation response simulation, event matching elimination, and differential reflection event set on the connected structure. (This is done while updating the connected structure.) Propagation response simulation was performed to obtain the simulated reflection event set for each measurement port. .Will and Events are matched and eliminated based on propagation distance and amplitude thresholds. Elimination can be made by Consistent interpretation of reflection events yields the differential reflection event set. The aforementioned The reflection component, which cannot be explained under the current structure, must have a propagation path that passes through unknown branches or unknown nodes that have not yet been incorporated into the model.
[0062] Step S6: Generation of the growth point set, distance update, and candidate growth distance set. Based on the updated connectivity structure... A set of growth points can be constructed at locations that can be expanded outwards. For any growth point ,Depend on Obtain the measurement port Direct distance to the growth point And perform distance updates on differential reflection events:
[0063]
[0064] Within the allowable error range, perform co-occurrence statistics on the update distances of different measurement ports to form a candidate growth distance set corresponding to the growth point. .
[0065] Step S7: Verification-based growth and global consistency evaluation screening. Let the current topology be... For any growth point Set its candidate growth distance The candidate distances are sorted in ascending order, and validation growth is performed sequentially at the growth point. A temporary external unknown branch is connected, the one-way length of which is half of the candidate growth distance (the candidate growth distance is the equivalent round-trip propagation distance of the reflection discontinuity at the end of the branch relative to the growth point), thus obtaining a temporary candidate topology. Calculate the global consistency evaluation of candidate topologies. and with the current topology of Compare:
[0066] when When the structure is expanded and updated, it is accepted. This immediately triggers a recalculation based on the updated topology: re-executes the propagation response simulation and event matching culling in step S5 to update the differential reflection event set. And re-execute step S6 to reconstruct the set of growth points. And the corresponding set of candidate growth distances. Due to the change in the interpretable propagation path of the topology, the previous set... Candidate distances generated by the coupling of unexplained reflection components and explained propagation paths may no longer appear after recalculation; however, candidate distances corresponding to the true unknown structure may be statistically obtained again after recalculation. After completing the above recalculation, the next round of growth iteration begins.
[0067] when When this happens, reject the candidate growth distance and maintain the current topology. Remain unchanged, and continue to try. The next candidate growth distance, until It has been completely traversed.
[0068] The processing order of growth points is not limited; after a structural update is accepted and a recalculation is triggered, the updated set of growth points can be processed again. Perform the above verification growth process.
[0069] Step S8: Termination and Output. When in the current growth point set... traversing each After that, none of them satisfy the condition. When the candidate structure is expanded, or the differential reflection event set is empty or no longer updated, the iteration stops and the topology inference result of the tree-like wired network is output.
[0070] In step S7 above, the distance consistency tolerance The event matching rejection threshold (including propagation distance threshold and amplitude threshold) and the threshold parameters used for cross-port consistency verification and co-occurrence statistics are all preset parameters. They can be determined based on factors such as the sampling rate and bandwidth of the measurement system, the resolution of reflection event extraction, the propagation speed estimation error, the noise level and the modeling error. This embodiment does not limit these parameters.
[0071] Distance-related thresholds can be uniformly set to the same distance tolerance. Alternatively, port pair consistency tolerance, event matching and elimination tolerance, and co-occurrence statistics tolerance can be set separately for different stages. The specific setting methods and example values can be given in the embodiments. This embodiment does not limit the specific values of the above thresholds and limits.
[0072] Furthermore, this embodiment does not limit the method for extracting the set of reflection events; it does not limit the method for obtaining the direct propagation distance; and it does not limit the initial connected structure. The construction algorithm is not limited; the specific line model and branch point equivalence method for propagation response simulation are not limited. As long as the direct propagation distance between measurement port pairs can be obtained and the set of reflection events at the measurement ports can be extracted, and the connectivity structure can be iteratively updated according to the above process of cross-port pair consistency verification, event matching elimination, differential-driven verification growth and global consistency evaluation screening, the topology inference of tree-like wired networks can be realized.
[0073] This embodiment follows steps S1–S8 as described in the "Technical Solution". To avoid repetition, only the parameter selection, data acquisition method, and specific implementation details for each step under example network conditions are given below. Any definitions, symbols, threshold meanings, and criteria that overlap with those in the Technical Solution shall be subject to the Technical Solution.
[0074] (1) Target audience and basic assumptions
[0075] This embodiment uses a typical tree-structured wired network as the implementation object to illustrate the specific implementation process of the method under engineering conditions. The tree-structured wired network is a loop-free connected structure, consisting of multiple wired branches and several intermediate connection nodes. Only some of the terminal ports in the network can be connected to measuring equipment, while the connection relationships of the remaining terminal ports and the internal network are unknown.
[0076] In practical engineering, the intermediate connection nodes in the tree-like wired network typically exhibit T-shaped or Y-shaped branch structures. During topology modeling and signal propagation analysis, these branch forms correspond topologically to the junction points of three line branches. Therefore, in this embodiment, the intermediate connection nodes are uniformly and equivalently abstracted as three-branch nodes.
[0077] Furthermore, there are several known end ports in the network that serve as measurement ports. In addition to the measurement ports, there may also be unknown end ports that are not connected for measurement. To facilitate the explanation of the implementation process of the method of the present invention, in this embodiment, it is assumed that the load impedance of the unknown end port is much greater than or much less than the characteristic impedance of the line, so that its corresponding reflection coefficient is approximately 1, thereby exhibiting strong reflection characteristics in the reflection response.
[0078] It should be noted that the above assumptions are only used to illustrate the specific implementation process of the method in this embodiment and do not constitute a limitation on the scope of protection of this application.
[0079] (2) Measurement port settings and acquisition of reflection event set
[0080] In this embodiment, measurement ports are set at some known end ports in the network to inject probe signals and receive reflection responses generated by the network impedance discontinuities; the probe signals can be pulses, frequency sweeps, or other signals that can extract propagation delay characteristics.
[0081] The reflection responses acquired from each measurement port are processed to extract reflection events (e.g., correlation / matched filtering, peak detection, etc.), and the corresponding set of reflection events is output according to technical solution S1. The set of reflection events includes at least the round-trip propagation distance characteristics and the reflection amplitude characteristics of the reflection events; to accommodate engineering errors, tolerances caused by resolution and noise are allowed in the distance and amplitude extraction.
[0082] (3) Obtaining the direct propagation distance between measurement port pairs
[0083] To obtain the direct propagation distance between measurement port pairs, this embodiment uses a pairwise mutual measurement method to collect the propagation response: a detection signal is injected into one measurement port, and the response corresponding to the direct propagation component is received at the other measurement port, and the data is repeatedly collected for different port combinations.
[0084] The first-reach propagation component is extracted from the propagation response to obtain the direct propagation delay. First-reach extraction can be achieved through time-domain first-reach peak detection or equivalent transformation and decision methods. Under multipath / reflection interference, stability can be enhanced by using time windows or threshold criteria. The direct delay is converted into direct distance based on the line propagation speed, which can be calculated from line parameters or obtained through calibration. The obtained direct distance is used to construct the direct propagation distance matrix of technical solution S2, allowing for the introduction of distance tolerance consistent with the technical solution.
[0085] (4) Construction of the initial connected structure and determination of the growth point
[0086] After obtaining the direct propagation distance matrix described in technical solution S2, this embodiment first constructs an initial connectivity structure at the measurement port level according to technical solution S3. The method for generating the initial connectivity structure is not limited; for ease of illustration, this embodiment uses the Rooted Node Join Algorithm (RNJA) to generate an acyclic connectivity skeleton based on the measurement ports as nodes and the direct propagation distance between ports, thereby obtaining an initial tree-like connectivity structure covering all measurement ports. To ensure that the initial connectivity structure can be stably generated and to avoid tree construction ambiguity, this embodiment assumes that the upper bound of the maximum estimation error of the direct propagation distance between measurement ports is less than half the length of the shortest branch in the target network; subsequent steps are all carried out under this premise.
[0087] Based on the initial connectivity structure, this embodiment performs cross-port consistency verification on port pair reflection events according to technical solution S4 to complete the implicit intermediate connection nodes in the initial connectivity structure and update the connectivity relationship: For each port pair connectivity path in the initial connectivity structure, the consistency matching is performed using the distance characteristics of the reflection events of the port pair and the corresponding direct propagation distance, and candidate connection positions that can be supported by multiple port pairs simultaneously are selected within the distance tolerance range; when a candidate position appears repeatedly among multiple port pairs and the consistency meets the technical solution criteria, it is confirmed as an intermediate connection point and the node is incorporated into the current connectivity structure to realize the refinement decomposition of the path and the structure update.
[0088] Furthermore, based on the updated connectivity structure, the growth point set is initialized according to the definition of "growth point" in the technical solution: Connecting nodes in the current connectivity structure that may undergo structural extension updates and their adjacent branch positions are selected as growth point candidates, providing a search starting point for the subsequent differential reflection event-driven unknown branch growth process. Distance tolerance related to consistency matching... The values should be consistent with the technical solution and can be set in combination with measurement resolution and noise level.
[0089] (5) Simulation of propagation response and extraction of differential reflection events based on updated connectivity structure
[0090] After identifying intermediate connecting nodes and obtaining the updated connectivity structure, this embodiment, under the aforementioned implementation objects and basic assumptions, performs propagation response simulation on the updated connectivity structure to determine the set of reflection events that should be observable at each measurement port under network conditions containing only this connectivity structure. By comparing and analyzing the simulated reflection events with the actual measured reflection events, reflection components that cannot be explained by the current updated connectivity structure can be extracted, providing a basis for the growth and localization of subsequent unknown branches.
[0091] The propagation response simulation is based on a transmission line network model. Specifically, on a defined updated connectivity structure, the propagation of the signal in the branches, reflection and transmission at intermediate connection nodes, and reflection at the terminal ports are modeled according to the physical length and propagation parameters of each branch. The multiple propagation and reflection processes of the signal in the network are solved to obtain the equivalent reflection response at the measurement port. This solution process can be implemented numerically, aiming to obtain the reflection response characteristics at the measurement port under the updated connectivity structure, without limiting the specific solution algorithm. The network topology input is the defined updated connectivity structure. The propagation characteristics of each branch are given by the characteristic impedance and propagation parameters of the power lines, which can be directly given by the power line properties or calibrated on-site. The load conditions at the measurement port and the boundary conditions at the unknown terminal ports are based on the settings explicitly given in the implementation object and basic assumptions.
[0092] For intermediate connection nodes in the updated connected structure, the equivalent impedance approximation is used for modeling in the propagation response simulation. Specifically, for the connection... The intermediate node of each branch, considering only the local neighborhood of that node and assuming that the characteristic impedance of each branch is... Under the given conditions, the equivalent input impedance at the node can be approximated as: Under the above approximation conditions, the reflection and transmission behavior of the signal at this node can be directly determined by transmission line theory, and the result depends only on the number of branches at the node. related.
[0093] Based on the above equivalent impedance approximation, when a signal is incident on this node from one of the branches, its voltage reflection coefficient on the incident branch can be expressed as:
[0094]
[0095] The voltage transmission coefficient to any other branch is:
[0096]
[0097] When the node is a three-branch structure ( When =3), the above relationship naturally degenerates into , .
[0098] Under this modeling condition, a propagation response simulation is performed on the updated connectivity structure, and the corresponding reflection response is obtained at the measurement port. Then, a reflection event extraction method consistent with the actual measurement is used to obtain the set of simulated reflection events from the simulated reflection response. For the measurement port... Let its simulated reflection event set be:
[0099]
[0100] in Indicates the first The round-trip propagation distance of a simulated reflection event. For the corresponding reflection amplitude characteristics, It represents the total number of events in the simulation set.
[0101] After obtaining the set of simulated reflection events, the set of real reflection events will be... With simulated reflection event set Differential processing is performed to remove reflection events already explained by the updated connectivity structure. A reflection event is considered explained only if it simultaneously satisfies the matching condition in both propagation distance and voltage amplitude characteristics. The differential set of reflection events is defined as follows:
[0102]
[0103] in, To match the tolerance threshold for propagation distance, A voltage amplitude matching tolerance threshold is set, which is determined by the detection accuracy and noise level. The differential reflection event set... For reflection components that cannot be explained by the updated connectivity structure, their propagation path must pass through unknown branches or nodes outside the updated connectivity structure. The symbol " The symbol "" indicates that all elements that also belong to the second set are "subtracted" or "removed" from the first set; "Used to separate the elements of a set from its defining condition;" "Yes" is an existential quantifier that declares that there is at least one element within a certain range that makes the following statement true.
[0104] (6) A method for identifying growth points and determining the set of growth distances based on the current network topology
[0105] Obtain the differential reflection event set for each measurement port. Subsequently, based on the set of growth points determined in the updated connectivity structure, a consistency screening is performed on the differential reflection events to determine the possible propagation distance information corresponding to the unknown branches extending outward from each growth point. This screening is performed in each round of processing based on the "current network topology" and is recalculated in the next round after the network topology is updated.
[0106] Let the set of growth points be:
[0107]
[0108] in, Indicates the first One growth point This represents the total number of growth points.
[0109] For any growth point The growth point to each measurement port can be directly obtained by updating the connectivity structure. The direct transmission distance between them is denoted as .
[0110] For measurement port The set of differential reflection events:
[0111]
[0112] For each of these reflection events, based on the growth point Perform distance update processing, subtracting the round-trip propagation distance from the measurement port to the growth point from the round-trip propagation distance, to obtain the equivalent propagation distance at the growth point:
[0113]
[0114] This allows for the construction of a measurement port. At the growth point The set of updated differential reflection events at the location:
[0115]
[0116] The updated differential reflection event set Instead of introducing new reflection events, it focuses on... The propagation distance in the data is remapped while its amplitude characteristics remain unchanged.
[0117] The physical meaning of the above distance update process is: if a certain differential reflection event is indeed caused by the growth point... If an unknown branch extends outward, then after deducting the round-trip propagation distance from the measurement port to the growth point, the reflection event should correspond to the same or approximately the same extensional propagation distance of the growth point at different measurement ports.
[0118] Based on the above principle, multiple measurement ports are located at the same growth point. The updated differential reflection event set obtained at the location Perform consistency screening. Within the allowable distance tolerance. Within the range, the common propagation distances in the updated differential reflection event sets at different measurement ports are statistically analyzed, and the growth point is determined accordingly. Corresponding growth distance set .
[0119] Specifically, the set of growth distances is defined as:
[0120]
[0121] The term "co-occurrence" refers to the fact that the update propagation distances from different measurement ports fall within the same distance tolerance range.
[0122] income Used to characterize growth points The potential propagation distance range of the outward-extending unknown branches serves as the basis for the subsequent generation and selection of candidate extensional subnetwork structures.
[0123] (7) Incremental growth method based on candidate distance of growth point for verification
[0124] Obtain the set of growth points in Section (6) and the set of growth distances corresponding to each growth point. This section then presents an update method for incorporating unknown branches into the current network topology. This invention iteratively processes the set of growth points; for any growth point... Set its candidate propagation distances Sort them in ascending order and process them one by one. The rationale is that the propagation distance of the first-reach component with a single reflection is shorter and should be verified first and used for topology growth; the longer candidate distances often contain components with multiple reflections or coupling with existing paths and should be processed after the shorter distances have been verified.
[0125] For any growth point The processing steps are as follows:
[0126] 1) Distance sorting: Element sorting ,in .
[0127] 2) Direct growth at intervals and immediate testing: for =1 to Execute in sequence:
[0128] 2.1) Based on the current network topology Based on the growth point Adding a new terminal branch yields a candidate topology. The length of the newly added branch road is... ;
[0129] 2.2) Calculate based on the global consistency cost function in Section (8) And compare:
[0130] like If the direct growth corresponding to that length does not reduce the interpretability, then the growth is accepted and the current topology is updated. Subsequently, propagation simulation and event matching were immediately performed based on the updated topology, the differential reflection event set was updated, and the growth point set was updated. Then, re-execute Section (VI) to obtain a new set of growth distances (including the new...). At this point, the original sorting sequence may become invalid, therefore the subsequent distances to the growth point are processed using the recalculated sequence. To be accurate, continue with this step.
[0131] like If the direct growth corresponding to that length would reduce the explanatory power, then that length should be removed. And keeping the current topology unchanged, continue processing the next candidate propagation distance. .
[0132] After processing all growth points in a loop, if no candidate propagation distance exists that can achieve the desired propagation distance at any growth point... If the number of nodes does not increase (or decreases), the iteration stops and the current network topology is output as the reconstruction result.
[0133] (8) Method for constructing the global consistency cost function
[0134] To evaluate the interpretability of candidate network topologies for measured multi-port reflection characteristics, this embodiment constructs a globally consistent cost function. And in Section (7), the acceptance of the increment is determined by comparing the changes in the cost function before and after the increment growth. Among them, the set of real reflection events for each measurement port. and candidate topology The set of simulated reflection events The acquisition and meaning of have been given in the previous text and will not be repeated in this section.
[0135] To reduce sensitivity to small estimation errors in propagation distance, following common practice, the set of discrete reflection events is mapped to a continuous reflection curve. Let:
[0136] a) This is a distance smoothing parameter used to characterize the uncertainty caused by factors such as distance estimation error and resolution limitations;
[0137] b) The maximum propagation distance of the observation window is determined by the measurement time window or system settings.
[0138] Then for the port The true reflection curve and the simulated reflection curve are defined as follows:
[0139]
[0140]
[0141] in, As the propagation distance independent variable, This represents the upper limit of the integration or the upper limit of the observation range in the dimension of propagation distance.
[0142] Based on the aforementioned continuous reflection curves, candidate topologies are constructed. Global consistency cost function:
[0143]
[0144] cost The smaller the value, the better the candidate topology. The more consistent the interpretation of the measured reflection characteristics of the multi-port system, the better.
[0145] The solutions of the embodiments of this application will be described in detail below with reference to the accompanying drawings and specific application examples.
[0146] This embodiment illustrates the execution process of the power line network topology reconstruction method based on multi-port reflection response consistency analysis described in this application through a specific example of a tree-like power line network. Using actual measured reflection data and port pair direct propagation distance as input, this embodiment sequentially demonstrates the basic connected network construction, intermediate node refinement, reflection simulation and differential analysis, and network expansion based on candidate growth and screening, to verify the feasibility of the method.
[0147] I. Example Network and Acquisition of Measurement Input
[0148] like Figure 3 As shown, the example power line network includes multiple end ports and intermediate branch nodes, where selected ports... The ports are treated as known measurement ports, while the remaining ports are treated as unknown ports.
[0149] a) Known port-to-port direct propagation distance matrix
[0150] Based on measurements or prior information, the direct propagation distance (one-way) between known measurement port pairs can be obtained as follows:
[0151]
[0152] Among them, matrix elements Indicates measurement port and The direct transmission distance between them.
[0153] b) Measurement port reflection event set
[0154] The reflection response is measured at each measurement port, and reflection events are extracted using peak detection. Each reflection event consists of a pair of parameters. It means that, among them For round-trip transmission distance, This corresponds to the measured amplitude of the reflection event.
[0155] 1) Measurement port The set of reflection events is:
[0156]
[0157] 2) Measurement port The set of reflection events is:
[0158]
[0159] 3) Measurement port The set of reflection events is:
[0160]
[0161] The direct propagation distance matrix and the set of reflection events together constitute the initial input of the topology reconstruction method. The amplitude of the reflection events is the specific value obtained from measurement, and it serves as the basis for amplitude consistency judgment in the subsequent consistency analysis and screening process.
[0162] II. Construction of Initial Connected Network Based on Tree Metrics
[0163] See Figure 4 Based on the known direct propagation distance matrix between measurement port pairs The connectivity between measurement ports is analyzed according to the tree metric constraint relationship described in Example 1.
[0164] In this example, port pairs , , The direct propagation distances are 38, 57, and 45, respectively, which satisfy the tree metric addition consistency constraint. Therefore, it can be determined that the three measurement ports are connected through the same intermediate branch node, forming a tree with the intermediate node as the connecting element. The initial tree-like connected network structure is the common branch point.
[0165] III. Intermediate Node Refinement and Initial Connectivity Update Based on Reflection Event Consistency
[0166] Given that the initial connected network is already determined, the paths in the initial connected network are further refined by combining the distance consistency of multi-port reflection events.
[0167] By path For example, the direct round-trip propagation distance of its port is =76. At the measurement port. and From the set of reflection events, multiple sets of reflection events satisfy 50+26=76 and 62+14=76, thus determining that there are multiple distinguishable intermediate reflectors on the path, and refining the path to include nodes. , The structure.
[0168] Similarly, by analyzing the path Consistency analysis of the reflection events can determine the existence of intermediate nodes on this path. .
[0169] Through the above refinement process, without changing the initial connected network topology, the distribution of intermediate nodes within the path is clarified, thus obtaining the following... Figure 5 The updated connected network is shown.
[0170] IV. Reflection Simulation and Differential Reflection Event Extraction Based on the Updated Connected Network
[0171] Based on the updated connected network, the propagation model described in Section 5 is used to measure the port. The reflection response is simulated to obtain a set of simulated reflection events that can be interpreted by the updated connected network.
[0172] Each simulated reflection event consists of a pair of parameters It means that, among them The round-trip propagation distance obtained from the simulation, This is the simulated amplitude corresponding to the reflection event. The simulated amplitude is obtained by introducing a small deviation based on the measured amplitude of the reflection event obtained in step one, in order to reflect the difference between the simulation results of the propagation model and the measured results, without affecting the subsequent screening process based on amplitude consistency.
[0173] In this embodiment, the set of interpretable simulated reflection events corresponding to each measurement port is as follows:
[0174] Measurement port :
[0175] Measurement port :
[0176] Measurement port :
[0177] By matching the simulated reflection event set with the measured reflection event set, and subtracting reflection events that can be explained by the updated connected network within a preset distance tolerance range, a differential reflection event set is obtained:
[0178] Measurement port :
[0179] Measurement port :
[0180] Measurement port :
[0181] The differential reflection event retains its propagation distance information while inheriting the amplitude information of the corresponding reflection event, and serves as the input for the subsequent construction of the differential event subset of growth points and the selection of candidate structures.
[0182] V. Construction of Growth Point Differential Event Subset, Measure-by-Measure Verification Growth and Update
[0183] Based on the updated connected network and differential reflection event set, intermediate nodes that may have unknown branch extensions are selected as growth points. In this embodiment, nodes are selected... With nodes This will be explained as a growth point.
[0184] For each growth point, the differential reflection event is first processed for distance update, and the updated distances from different measurement ports are screened for consistency within the distance tolerance range, forming a candidate propagation distance set corresponding to that growth point. For example, we can obtain:
[0185] growth point : {46,58,94}
[0186] growth point : {32}
[0187] Subsequently, the candidate propagation distances obtained at any growth point are sorted in ascending order, and network expansion is performed using a step-by-step verification-based incremental growth method: For the current shortest candidate propagation distance, an unknown terminal branch is first appended to the growth point to form a candidate topology, where the one-way length of the appended branch is half of the candidate propagation distance (the candidate propagation distance corresponds to the round-trip propagation distance feature); then, the interpretability of the candidate topology before and after growth for multi-port measured reflection events is calculated and compared based on the global consistency cost function. When the appended candidate topology makes the global consistency cost not increase (or decrease), the growth is considered not to reduce the interpretability, the growth is accepted, and the appended branch is incorporated into the current network topology; when the appended candidate topology increases the global consistency cost, the candidate propagation distance is considered not to support the current structure update, the candidate propagation distance is discarded, and the next candidate propagation distance is verified.
[0188] It should be noted that once the circumscribed growth corresponding to a candidate propagation distance is accepted and the topology is updated, the propagation response simulation and event matching elimination process is immediately re-executed based on the updated topology to update the differential reflection event set and the growth point set simultaneously. Then, the distance update and consistency screening are performed again to obtain a new candidate propagation distance set, and the above-mentioned distance-by-distance verification growth process continues on the basis of the new candidate set.
[0189] By iteratively performing the above process of "candidate distance sorting - circumscribed growth - instant consistency check - update and recalculate upon passing" on each growth point, unknown branch structures can be gradually incorporated into the current network until the differential reflection event set is empty or there are no longer any structural updates that can keep the consistency cost from increasing, thereby completing the complete topology reconstruction of the example network.
[0190] In summary, compared with the prior art, the method of this embodiment has at least the following advantages and beneficial effects:
[0191] 1) Reduced deployment threshold and cost: Measurements can be performed on only a limited number of accessible ports on the network, eliminating the need for full network deployment, which greatly improves the applicability of the method in practical engineering.
[0192] 2) Improve the robustness and accuracy of inference: Intermediate nodes are confirmed through "cross-port consistency verification" to avoid errors caused by single-port or accidental matching; the explained part is removed through "simulation and difference" mechanism to make the inference target clearer; and the closed-loop iteration of "verification growth" and "global consistency evaluation" ensures that each structural expansion can improve the model's overall interpretability of all observation data, thereby obtaining more reliable and unique topological results.
[0193] 3) Controlling computational complexity: Driven by differential reflection events, the algorithm makes targeted expansion attempts only at candidate growth points and combines them with real-time evaluation for pruning, avoiding exhaustive search. This makes the algorithm complexity more related to the size of the unknown part of the network, rather than the entire network size, and is feasible in complex networks.
[0194] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.
[0195] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.
[0196] Please see Figure 6 , Figure 6 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:
[0197] The processor 601 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.
[0198] The memory 602 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 602 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 602 and is called and executed by the processor 601 using the methods described in the embodiments of this application.
[0199] The input / output interface 603 is used to implement information input and output;
[0200] The communication interface 604 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).
[0201] Bus 605 transmits information between various components of the device (e.g., processor 601, memory 602, input / output interface 603, and communication interface 604);
[0202] The processor 601, memory 602, input / output interface 603, and communication interface 604 are connected to each other within the device via bus 605.
[0203] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.
[0204] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.
[0205] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
[0206] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.
[0207] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented in the embodiments of this program product are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments. The executable computer program code or "code" used to perform the various embodiments can be written in high-level programming languages such as C, C++, Python, Smalltalk, Java, JavaScript, Visual Basic, Structured Query Language (e.g., Transact-SQL), Perl, or in various other programming languages.
[0208] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.
[0209] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.
[0210] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.
[0211] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.
[0212] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0213] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.
[0214] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
[0215] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0216] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0217] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0218] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.
Claims
1. A method for topology inference of a tree-like wired network, characterized in that, The method includes the following steps: Acquisition steps: From the M selected measurement ports in the tree-structured wired network, obtain the set of real reflection events corresponding to each measurement port and the direct propagation distance between each pair of the M measurement ports, where M is an integer greater than 1; Construction steps: Based on the direct propagation distance, construct a tree-like initial connectivity structure containing all M measurement ports; Update steps: Based on the cross-port consistency between the actual reflection events of different measurement port pairs, identify and add at least one intermediate connection point in the initial connectivity structure to form an updated connectivity structure; Simulation and differential steps: Perform propagation response simulation on the updated connectivity structure to obtain the set of simulated reflection events corresponding to each measurement port; match the set of real reflection events for each port with the set of simulated reflection events corresponding to the port, and remove reflection events that can be explained by the updated connectivity structure to obtain the set of differential reflection events corresponding to each measurement port. Growth and verification steps: Based on the updated connectivity structure, at least one growth point is determined, and a candidate growth distance set is generated at each growth point based on the differential reflection event set; driven by the candidate growth distance set, verification-based structure expansion is performed at the corresponding growth point, and the expanded candidate topology structure is filtered using the global consistency evaluation function, and the current topology structure is iteratively updated. Termination step: When the preset termination condition is met, stop the iteration and output the current topology as the inferred topology of the tree-like wired network.
2. The method according to claim 1, characterized in that, The update steps specifically include: For at least one pair of measurement ports, a pair of reflection events is selected from their respective sets of real reflection events. Based on a first distance consistency constraint between the round-trip propagation distance of the reflection event pair and the direct propagation distance of the corresponding port pair, candidate connection positions on the path of the port pair are determined. When the same candidate connection position is determined by at least two different measurement ports based on the distance consistency constraint, and the determined candidate connection positions of each port match each other within a preset distance tolerance range, the candidate connection position is determined as an intermediate connection point, and the intermediate connection point is added to the initial connectivity structure.
3. The method according to claim 1, characterized in that, In the simulation and difference steps, the condition for determining that a real reflection event can be explained by the updated connectivity structure is: In the set of simulated reflection events, there exists a simulated reflection event such that the difference between the round-trip propagation distance between the real reflection event and the simulated reflection event does not exceed a first distance tolerance threshold, and the difference in reflection amplitude does not exceed an amplitude tolerance threshold.
4. The method according to claim 1, characterized in that, In the growth and verification steps, the differential reflection event set is based on the growth point. Generate a set of candidate growth distances. Specifically, it includes: For each measurement port Calculate the update distance for each event in its differential reflection event set, where the update distance is the round-trip propagation distance of the event minus twice the measurement port distance. To the growth point The distance; Within the preset second distance tolerance range, the distance values that occur together from the updated distances of different measurement ports are statistically analyzed; The common distance values are used to form the growth points. The corresponding candidate growth distance set .
5. The method according to claim 1, characterized in that, In the growth and verification steps, at the growth point The specific implementation of the verification-based structure extension includes: The candidate growth distance set Candidate distances are sorted in ascending order of value; Try each candidate distance in order of sorting. At the growth point A temporary external connection with a length of Candidate branches are used to form a temporary candidate topology; Calculate the global consistency evaluation function value of the temporary candidate topology. ; like ,in If the global consistency evaluation function value of the current topology is found to be true, then the temporary candidate topology is accepted as the new current topology, and the simulation and differential steps, as well as the operation of generating the candidate growth distance set in this step, are re-executed to update the differential reflection event set and the candidate growth distance set before continuing the iteration; otherwise, the candidate distance is rejected. And try the next candidate distance.
6. The method according to claim 5, characterized in that, The global consistency evaluation function The construction method is as follows: For each measurement port The actual set of reflection events and the simulated set of reflection events under the current topology S are respectively transformed into continuous reflection amplitude curves and; The integral sum of the squares of the differences between the reflection amplitude curves and the simulated amplitude curves at all measurement ports over the observation distance range is used as the global consistency evaluation function. The value of .
7. The method according to claim 1, characterized in that, In the construction step, the tree-like initial connected structure is constructed based on the direct propagation distance. This is achieved through the root adjacency algorithm or the minimum spanning tree algorithm, and the constructed structure satisfies the following condition: the path length between any two measurement ports in the structure is consistent with the direct propagation distance between them within the allowable error range.
8. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 7.
9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.
10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.